Publications
Publications
- July 2019 (Revised November 2019)
- HBS Case Collection
Osaro: Picking the Best Path
By: William R. Kerr, James Palano and Bastiane Huang
Abstract
The founder of Osaro saw the potential of deep reinforcement learning to allow robots to be applied to new applications. Osaro targeted warehousing, already a dynamic industry for robotics and automation, for its initial product—a system which would allow robotic arms to “pick and place” individual items into boxes that would be shipped to consumers. Despite receiving significant attention in the robotics space, the problem of universal “picking” had not been solved. Osaro believed deep reinforcement learning was the key to allowing robots to handle the complexity involved in perceiving and grasping a wide range of objects. The case discussion will focus on the decision to choose the warehouse automation market, provide an overview of key machine learning concepts, and introduce students to some of the contours of competing in the machine learning space during the late 2010s. The case can also introduce key concepts for thinking about how the combination of artificial intelligence and robotics will change work in the future.
Keywords
Artificial Intelligence; Machine Learning; Robotics; Robots; Ecommerce; Fulfillment; Warehousing; AI; Startup; Technology Commercialization; Business Startups; Entrepreneurship; Logistics; Order Taking and Fulfillment; Information Technology; Commercialization; Learning; Complexity; Competition; E-commerce
Citation
Kerr, William R., James Palano, and Bastiane Huang. "Osaro: Picking the Best Path." Harvard Business School Case 820-012, July 2019. (Revised November 2019.)